Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is significantly hindered by laborious data annotation. Given this challenge, recent research has predominantly focused on two approaches: pretraining task-oriented models for event extraction and employing data augmentation techniques. These methods involve integrating external knowledge, semantic structures, or artificially generated samples using large language models (LLMs). However, their performances can be compromised due to two fundamental issues. Firstly, the alignment between the introduced knowledge and event extraction knowledge is crucial. Secondly, the introduction of data noise during the augmentation is unavoidable and can mislead the model’s convergence. To address these issues, we propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction. Different from the existing methods introducing linguistic knowledge into data augmentation, an event aggregation network is established to introduce event knowledge into supervised learning by constructing adaptively-updated semantic representation for trigger and argument. For LLM-based augmentation, we design a new scheme including a multi-pattern rephrasing paradigm and a data-free composing paradigm. Instead of directly using augmentation samples in the supervised task, we introduce span-level contrastive learning to reduce data noise. Experiments on the ACE2005 and ERE-EN demonstrate that our proposed approach achieves new state-of-the-art results on both of the two datasets.